Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xvii 7. Acknowledgment - p. xxvii 8. Chapter 1: Image synthesis and superresolution in medical imaging - p. 1 9. Chapter 2: Machine learning for image reconstruction - p. 25 10. Chapter 3: Liver lesion detection in CT using deep learning techniques - p. 65 11. Chapter 4: CAD in lung - p. 91 12. Chapter 5: Text mining and deep learning for disease classification - p. 109 13. Chapter 6: Multiatlas segmentation - p. 137 14. Chapter 7: Segmentation using adversarial image-to-image networks - p. 165 15. Chapter 8: Multimodal medical volumes translation and segmentation with generative adversarial network - p. 183 16. Chapter 9: Landmark detection and multiorgan segmentation: Representations and supervised approaches - p. 205 17. Chapter 10: Deep multilevel contextual networks for biomedical image segmentation - p. 231 18. Chapter 11: LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction - p. 249 19. Chapter 12: Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics - p. 273 20. Chapter 13: Image registration with sliding motion - p. 293 21. Chapter 14: Image registration using machine and deep learning - p. 319 22. Chapter 15: Imaging biomarkers in Alzheimer's disease - p. 343 23. Chapter 16: Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective - p. 379 24. Chapter 17: Imaging biomarkers for cardiovascular diseases - p. 401 25. Chapter 18: Radiomics - p. 429 26. Chapter 19: Random forests in medical image computing - p. 457 27. Chapter 20: Convolutional neural networks - p. 481 28. Chapter 21: Deep learning: RNNs and LSTM - p. 503 29. Chapter 22: Deep multiple instance learning for digital histopathology - p. 521 30. Chapter 23: Deep learning: Generative adversarial networks and adversarial methods - p. 547 31. Chapter 24: Linear statistical shape models and landmark location - p. 575 32. Chapter 25: Computer-integrated interventional medicine: A 30 year perspective - p. 599 33. Chapter 26: Technology and applications in interventional imaging: 2D X-ray radiography/fluoroscopy and 3D cone-beam CT - p. 625 34. Chapter 27: Interventional imaging: MR - p. 673 35. Chapter 28: Interventional imaging: Ultrasound - p. 701 36. Chapter 29: Interventional imaging: Vision - p. 721 37. Chapter 30: Interventional imaging: Biophotonics - p. 747 38. Chapter 31: External tracking devices and tracked tool calibration - p. 777 39. Chapter 32: Image-based surgery planning - p. 795 40. Chapter 33: Human–machine interfaces for medical imaging and clinical interventions - p. 817 41. Chapter 34: Robotic interventions - p. 841 42. Chapter 35: System integration - p. 861 43. Chapter 36: Clinical translation - p. 893 44. Chapter 37: Interventional procedures training - p. 909 45. Chapter 38: Surgical data science - p. 931 46. Chapter 39: Computational biomechanics for medical image analysis - p. 953 47. Chapter 40: Challenges in Computer Assisted Interventions - p. 979 48. Index - p. 1013
Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Contributors - p. xvii 7. Acknowledgment - p. xxvii 8. Chapter 1: Image synthesis and superresolution in medical imaging - p. 1 9. Chapter 2: Machine learning for image reconstruction - p. 25 10. Chapter 3: Liver lesion detection in CT using deep learning techniques - p. 65 11. Chapter 4: CAD in lung - p. 91 12. Chapter 5: Text mining and deep learning for disease classification - p. 109 13. Chapter 6: Multiatlas segmentation - p. 137 14. Chapter 7: Segmentation using adversarial image-to-image networks - p. 165 15. Chapter 8: Multimodal medical volumes translation and segmentation with generative adversarial network - p. 183 16. Chapter 9: Landmark detection and multiorgan segmentation: Representations and supervised approaches - p. 205 17. Chapter 10: Deep multilevel contextual networks for biomedical image segmentation - p. 231 18. Chapter 11: LOGISMOS-JEI: Segmentation using optimal graph search and just-enough interaction - p. 249 19. Chapter 12: Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics - p. 273 20. Chapter 13: Image registration with sliding motion - p. 293 21. Chapter 14: Image registration using machine and deep learning - p. 319 22. Chapter 15: Imaging biomarkers in Alzheimer's disease - p. 343 23. Chapter 16: Machine learning based imaging biomarkers in large scale population studies: A neuroimaging perspective - p. 379 24. Chapter 17: Imaging biomarkers for cardiovascular diseases - p. 401 25. Chapter 18: Radiomics - p. 429 26. Chapter 19: Random forests in medical image computing - p. 457 27. Chapter 20: Convolutional neural networks - p. 481 28. Chapter 21: Deep learning: RNNs and LSTM - p. 503 29. Chapter 22: Deep multiple instance learning for digital histopathology - p. 521 30. Chapter 23: Deep learning: Generative adversarial networks and adversarial methods - p. 547 31. Chapter 24: Linear statistical shape models and landmark location - p. 575 32. Chapter 25: Computer-integrated interventional medicine: A 30 year perspective - p. 599 33. Chapter 26: Technology and applications in interventional imaging: 2D X-ray radiography/fluoroscopy and 3D cone-beam CT - p. 625 34. Chapter 27: Interventional imaging: MR - p. 673 35. Chapter 28: Interventional imaging: Ultrasound - p. 701 36. Chapter 29: Interventional imaging: Vision - p. 721 37. Chapter 30: Interventional imaging: Biophotonics - p. 747 38. Chapter 31: External tracking devices and tracked tool calibration - p. 777 39. Chapter 32: Image-based surgery planning - p. 795 40. Chapter 33: Human–machine interfaces for medical imaging and clinical interventions - p. 817 41. Chapter 34: Robotic interventions - p. 841 42. Chapter 35: System integration - p. 861 43. Chapter 36: Clinical translation - p. 893 44. Chapter 37: Interventional procedures training - p. 909 45. Chapter 38: Surgical data science - p. 931 46. Chapter 39: Computational biomechanics for medical image analysis - p. 953 47. Chapter 40: Challenges in Computer Assisted Interventions - p. 979 48. Index - p. 1013
